Neural networks and neural network applications are known in the art. Experiments in biological neural network have determined that the strength of synaptic connections between neurons in the brain is a function of the frequency of excitation. Neurons are presented with numerous stimuli (input signals, produced by some external action, such as the eye viewing an object, or the skin sensing temperature). After sufficient exposure to sensorial stimuli from an environment, a collection of neurons will start to react differently, depending on the strength of the individual stimuli. One effect of this process is that certain neurons, or collections of neurons, are more likely to fire when presented with certain patterns rather than others. The same collection of neurons is also sensitive to patterns that are fairly similar. This sensitivity can over time be construed as ‘learning’ a certain part of an input space.
T. Kohonen has created one mathematical abstraction of the above-described neural network process, known as the Kohonen algorithm, which is discussed in detail in various writings. The Kohonen algorithm has been used to model simple models of the cortex and has also been used in other applications. However, present applications have not addressed all of the needs related to computer implemented data analysis using neural network models.